Say you have a lot of PDF documents, say K documents.
Each document Di is Ni pages long. In one of the Ni pages (don't know which, say Pi), there is the information you need to extract.
I am thinking about building a three step pipeline for this:
Extract a random sample of Krand documents from your data.
Annotate all the pages into two categories (relevant/irrelevant)
Fine tune a document classification model on this data
Extract relevant pages from all documents using the document classification model.
Annotate a sample of relevant pages and fine tune an object detection model to recognize your Region of Interest in that document.
Run OCR on your ROIs.
Post-process the OCR outputs.
Will highly appreciate your comments and suggestions on which Document Classification model and Object Detection model will be the best and whether this is a right approach or not.